Developing the big data and analytics maturity model
A recent posting introduced and described the big data and analytics maturity model. The model provides a framework for a coherent approach to big data and analytics across an enterprise, one that is business-driven and able to adapt to evolving business objectives.
The model has been enhanced since its adoption in early 2014. The big data and analytics maturity model has been used to facilitate and drive discussions in enterprises across a range of industries including retail, telecommunications and utilities, to name a few. Some of these enterprises look to big data and analytics for improved customer interaction, and others—through rich insights—seek operational efficiency gains within their own organizations and in partnership with others.
In working with the model, a large financial services institution, for instance, helped us further assess the internal linkages between business processes and insights. The model was updated, and at best an enterprise can have fully immersed insights provided in a real-time, actionable context across key processes.
In a similar development, an Australian telecommunications organization encouraged us to focus on the cultural changes necessary for big data and analytics initiatives to succeed. Traditionally, organizations had time to work and respond to information, but accurate insights can now be provided in real-time, forcing organizations onto the front foot.
Furthermore, we can also expect machines to take on more decision making. The big data and analytics maturity model helps measure cultural adaptability. In the previously mentioned posting we referred to the IBM Institute of Business Value study, Analytics: The speed advantage on this topic.
Early experiences using the model at a UK retailer were focused on information governance. The model helped to identify immediate activities necessary to enable successful application of analytics insight. The business drivers were focused on putting in place digital capabilities to help improve the customer experience. The retailer recognized that these capabilities needed to be underpinned with analytics, but use of the model showed that some improvements to information governance were required first to successfully exploit analytics.
There has been much discussion in recent months within IBM on the growing usage of cognitive computing and how it relates to big data and analytics. We have started to outline cognitive computing maturity and look at an enterprise’s big data and analytics platform as being a supportive, interlinked enabler of cognitive capabilities such as IBM Watson.
Another topic of discussion, both within IBM and with customers, has been around ethics. Laws and regulations guide organizations, particularly around privacy and the use of data, defining the current no-go areas for an organization. Recent advances in analytics and big data technology, however, have widened the gap between what is possible and what is legally allowed, bringing about potential consequences for organizations if not examined carefully. We have included a section on ethics in the model to reflect this important matter.
As you can see, the improvements to the model are continuous, thanks to feedback from organizations IBM works with and IBM colleagues. We also welcome thoughts from you, our readers, on our efforts. Look for an upcoming posting in this series that discusses how the model can be applied to help realize competitive business value. Meanwhile, please read the paper on ethics best practices that covers the importance of the ethics of analytics and the need to appropriately handle the sensitivity of the data we work with.